Optic Disc Segmentation in Retinal Fundus Images Using Fully Convolutional Network and Removal of False-Positives Based on Shape Features

In today's world blindness is a major concern in working population and diseases like glaucoma, diabetic retinopathy are main causes for this. Early and fast detection using automated software system can be a great help in this area. For that one major step is to detect and segment the optic disc (OD) in retinal fundus image. In this paper we have used U-Net based fully convolutional network to segment OD. U-Net is a very efficient architecture in image segmentation particularly in the area where availability of input images are very less. We have first trained U-Net from scratch on the extended Messidor dataset. It is then evaluated using three-fold cross validation on MESSIDOR image dataset. During the process we have removed false positives based on morphological operation and shape features. We have seen this method has outperformed existing techniques in OD segmentation on the images affected by diabetic retinopathy.